Dyrskjøt Lars, Thykjaer Thomas, Kruhøffer Mogens, Jensen Jens Ledet, Marcussen Niels, Hamilton-Dutoit Stephen, Wolf Hans, Orntoft Torben F
Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, DK-8200 Aarhus N, Denmark.
Nat Genet. 2003 Jan;33(1):90-6. doi: 10.1038/ng1061. Epub 2002 Dec 9.
Bladder cancer is a common malignant disease characterized by frequent recurrences. The stage of disease at diagnosis and the presence of surrounding carcinoma in situ are important in determining the disease course of an affected individual. Despite considerable effort, no accepted immunohistological or molecular markers have been identified to define clinically relevant subsets of bladder cancer. Here we report the identification of clinically relevant subclasses of bladder carcinoma using expression microarray analysis of 40 well characterized bladder tumors. Hierarchical cluster analysis identified three major stages, Ta, T1 and T2-4, with the Ta tumors further classified into subgroups. We built a 32-gene molecular classifier using a cross-validation approach that was able to classify benign and muscle-invasive tumors with close correlation to pathological staging in an independent test set of 68 tumors. The classifier provided new predictive information on disease progression in Ta tumors compared with conventional staging (P < 0.005). To delineate non-recurring Ta tumors from frequently recurring Ta tumors, we analyzed expression patterns in 31 tumors by applying a supervised learning classification methodology, which classified 75% of the samples correctly (P < 0.006). Furthermore, gene expression profiles characterizing each stage and subtype identified their biological properties, producing new potential targets for therapy.
膀胱癌是一种常见的恶性疾病，其特点是频繁复发。诊断时的疾病分期以及周围原位癌的存在对于确定受影响个体的病程很重要。尽管付出了巨大努力，但尚未确定用于定义膀胱癌临床相关亚组的公认免疫组织化学或分子标志物。在此，我们报告了通过对40个特征明确的膀胱肿瘤进行表达微阵列分析来鉴定膀胱癌的临床相关亚类。层次聚类分析确定了三个主要阶段，Ta、T1和T2-4，其中Ta肿瘤进一步分为亚组。我们使用交叉验证方法构建了一个32基因分子分类器，该分类器能够在一个包含68个肿瘤的独立测试集中，将良性和肌层浸润性肿瘤与病理分期紧密相关地进行分类。与传统分期相比，该分类器提供了关于Ta肿瘤疾病进展的新预测信息（P < 0.005）。为了区分非复发性Ta肿瘤和频繁复发性Ta肿瘤，我们应用监督学习分类方法分析了31个肿瘤的表达模式，该方法正确分类了75%的样本（P < 0.006）。此外，表征每个阶段和亚型的基因表达谱确定了它们的生物学特性，为治疗产生了新的潜在靶点。
